56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
19 lines
319 B
Python
Executable File
19 lines
319 B
Python
Executable File
#!/usr/bin/env python3
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import cccl.bench as bench
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# TODO:
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# - driver version
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# - host compiler + version
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# - gpu clocks / pm
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# - ecc
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def main():
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center_estimator = bench.MedianCenterEstimator()
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bench.search(bench.BruteForceSeeker(center_estimator, center_estimator))
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if __name__ == "__main__":
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main()
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